Agenda

  • Introduction to time series analysis and forecasting
  • Time series objects - introduction to the time series classes and their attributes
  • Descriptive analysis of time series
  • Linear regression-based forecasting models
  • The ARIMA family of models

Admin

All today’s slides, code, and rmarkdown files are available here

Downloading the workshop material from the terminal:

git clone https://github.com/RamiKrispin/Time-Series-Workshop.git

Or lunch it on a docker container:

Introduction to time series analysis and forecasting

Time series data

Time series data - is a sequence of values, each associate to a unique point in time

Regular time series - is a sequence of observations which were captured at equally spaced time intervals (e.g., every month, week, day, hour, etc.)

Irregular time series - or unevenly spaced time series, is a sequence of observations which were not captured on equally spaced time intervals (for example rainy days, earthquakes, etc.)

Note: typically, the term time series data referred to regular time-series data.

Examples of time series data

Time Series Analysis

Time series analysis - is the process of extracting meaningful insights from time series data in order to learn about the past and predict the future